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How to use hidden method in Lemoncheesecake

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stt_layer_lstm.py

Source:stt_layer_lstm.py

1# Licensed to the Apache Software Foundation (ASF) under one2# or more contributor license agreements.  See the NOTICE file3# distributed with this work for additional information4# regarding copyright ownership.  The ASF licenses this file5# to you under the Apache License, Version 2.0 (the6# "License"); you may not use this file except in compliance7# with the License.  You may obtain a copy of the License at8#9#   http://www.apache.org/licenses/LICENSE-2.010#11# Unless required by applicable law or agreed to in writing,12# software distributed under the License is distributed on an13# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY14# KIND, either express or implied.  See the License for the15# specific language governing permissions and limitations16# under the License.17# pylint:skip-file18from collections import namedtuple19import mxnet as mx20from stt_layer_batchnorm import batchnorm21LSTMState = namedtuple("LSTMState", ["c", "h"])22LSTMParam = namedtuple("LSTMParam", ["i2h_weight", "i2h_bias",23                                     "h2h_weight", "h2h_bias",24                                     "ph2h_weight",25                                     "c2i_bias", "c2f_bias", "c2o_bias"])26LSTMModel = namedtuple("LSTMModel", ["rnn_exec", "symbol",27                                     "init_states", "last_states",28                                     "seq_data", "seq_labels", "seq_outputs",29                                     "param_blocks"])30def vanilla_lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, is_batchnorm=False, gamma=None, beta=None, name=None):31    """LSTM Cell symbol"""32    i2h = mx.sym.FullyConnected(data=indata,33                                weight=param.i2h_weight,34                                bias=param.i2h_bias,35                                num_hidden=num_hidden * 4,36                                name="t%d_l%d_i2h" % (seqidx, layeridx))37    if is_batchnorm:38        if name is not None:39            i2h = batchnorm(net=i2h, gamma=gamma, beta=beta, name="%s_batchnorm" % name)40        else:41            i2h = batchnorm(net=i2h, gamma=gamma, beta=beta)42    h2h = mx.sym.FullyConnected(data=prev_state.h,43                                weight=param.h2h_weight,44                                bias=param.h2h_bias,45                                num_hidden=num_hidden * 4,46                                name="t%d_l%d_h2h" % (seqidx, layeridx))47    gates = i2h + h2h48    slice_gates = mx.sym.SliceChannel(gates, num_outputs=4,49                                      name="t%d_l%d_slice" % (seqidx, layeridx))50    in_gate = mx.sym.Activation(slice_gates[0], act_type="sigmoid")51    in_transform = mx.sym.Activation(slice_gates[1], act_type="tanh")52    forget_gate = mx.sym.Activation(slice_gates[2], act_type="sigmoid")53    out_gate = mx.sym.Activation(slice_gates[3], act_type="sigmoid")54    next_c = (forget_gate * prev_state.c) + (in_gate * in_transform)55    next_h = out_gate * mx.sym.Activation(next_c, act_type="tanh")56    return LSTMState(c=next_c, h=next_h)57def lstm(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0., num_hidden_proj=0, is_batchnorm=False,58         gamma=None, beta=None, name=None):59    """LSTM Cell symbol"""60    # dropout input61    if dropout > 0.:62        indata = mx.sym.Dropout(data=indata, p=dropout)63    i2h = mx.sym.FullyConnected(data=indata,64                                weight=param.i2h_weight,65                                bias=param.i2h_bias,66                                num_hidden=num_hidden * 4,67                                name="t%d_l%d_i2h" % (seqidx, layeridx))68    if is_batchnorm:69        if name is not None:70            i2h = batchnorm(net=i2h, gamma=gamma, beta=beta, name="%s_batchnorm" % name)71        else:72            i2h = batchnorm(net=i2h, gamma=gamma, beta=beta)73    h2h = mx.sym.FullyConnected(data=prev_state.h,74                                weight=param.h2h_weight,75                                # bias=param.h2h_bias,76                                no_bias=True,77                                num_hidden=num_hidden * 4,78                                name="t%d_l%d_h2h" % (seqidx, layeridx))79    gates = i2h + h2h80    slice_gates = mx.sym.SliceChannel(gates, num_outputs=4,81                                      name="t%d_l%d_slice" % (seqidx, layeridx))82    Wcidc = mx.sym.broadcast_mul(param.c2i_bias, prev_state.c) + slice_gates[0]83    in_gate = mx.sym.Activation(Wcidc, act_type="sigmoid")84    in_transform = mx.sym.Activation(slice_gates[1], act_type="tanh")85    Wcfdc = mx.sym.broadcast_mul(param.c2f_bias, prev_state.c) + slice_gates[2]86    forget_gate = mx.sym.Activation(Wcfdc, act_type="sigmoid")87    next_c = (forget_gate * prev_state.c) + (in_gate * in_transform)88    Wcoct = mx.sym.broadcast_mul(param.c2o_bias, next_c) + slice_gates[3]89    out_gate = mx.sym.Activation(Wcoct, act_type="sigmoid")90    next_h = out_gate * mx.sym.Activation(next_c, act_type="tanh")91    if num_hidden_proj > 0:92        proj_next_h = mx.sym.FullyConnected(data=next_h,93                                            weight=param.ph2h_weight,94                                            no_bias=True,95                                            num_hidden=num_hidden_proj,96                                            name="t%d_l%d_ph2h" % (seqidx, layeridx))97        return LSTMState(c=next_c, h=proj_next_h)98    else:99        return LSTMState(c=next_c, h=next_h)100def lstm_unroll(net, num_lstm_layer, seq_len, num_hidden_lstm_list, dropout=0., num_hidden_proj=0,101                lstm_type='fc_lstm', is_batchnorm=False, prefix="", direction="forward", is_bucketing=False):102    if num_lstm_layer > 0:103        param_cells = []104        last_states = []105        for i in range(num_lstm_layer):106            param_cells.append(LSTMParam(i2h_weight=mx.sym.Variable(prefix + "l%d_i2h_weight" % i),107                                         i2h_bias=mx.sym.Variable(prefix + "l%d_i2h_bias" % i),108                                         h2h_weight=mx.sym.Variable(prefix + "l%d_h2h_weight" % i),109                                         h2h_bias=mx.sym.Variable(prefix + "l%d_h2h_bias" % i),110                                         ph2h_weight=mx.sym.Variable(prefix + "l%d_ph2h_weight" % i),111                                         c2i_bias=mx.sym.Variable(prefix + "l%d_c2i_bias" % i,112                                                                  shape=(1, num_hidden_lstm_list[i])),113                                         c2f_bias=mx.sym.Variable(prefix + "l%d_c2f_bias" % i,114                                                                  shape=(1, num_hidden_lstm_list[i])),115                                         c2o_bias=mx.sym.Variable(prefix + "l%d_c2o_bias" % i,116                                                                  shape=(1, num_hidden_lstm_list[i]))117                                         ))118            state = LSTMState(c=mx.sym.Variable(prefix + "l%d_init_c" % i),119                              h=mx.sym.Variable(prefix + "l%d_init_h" % i))120            last_states.append(state)121        assert (len(last_states) == num_lstm_layer)122        # declare batchnorm param(gamma,beta) in timestep wise123        if is_batchnorm:124            batchnorm_gamma = []125            batchnorm_beta = []126            if is_bucketing:127                for l in range(num_lstm_layer):128                    batchnorm_gamma.append(mx.sym.Variable(prefix + "l%d_i2h_gamma" % l))129                    batchnorm_beta.append(mx.sym.Variable(prefix + "l%d_i2h_beta" % l))130            else:131                for seqidx in range(seq_len):132                    batchnorm_gamma.append(mx.sym.Variable(prefix + "t%d_i2h_gamma" % seqidx))133                    batchnorm_beta.append(mx.sym.Variable(prefix + "t%d_i2h_beta" % seqidx))134        hidden_all = []135        for seqidx in range(seq_len):136            if direction == "forward":137                k = seqidx138                hidden = net[k]139            elif direction == "backward":140                k = seq_len - seqidx - 1141                hidden = net[k]142            else:143                raise Exception("direction should be whether forward or backward")144            # stack LSTM145            for i in range(num_lstm_layer):146                if i == 0:147                    dp = 0.148                else:149                    dp = dropout150                if lstm_type == 'fc_lstm':151                    if is_batchnorm:152                        if is_bucketing:153                            next_state = lstm(num_hidden_lstm_list[i],154                                              indata=hidden,155                                              prev_state=last_states[i],156                                              param=param_cells[i],157                                              seqidx=k,158                                              layeridx=i,159                                              dropout=dp,160                                              num_hidden_proj=num_hidden_proj,161                                              is_batchnorm=is_batchnorm,162                                              gamma=batchnorm_gamma[i],163                                              beta=batchnorm_beta[i],164                                              name=prefix + ("t%d_l%d" % (seqidx, i))165                                              )166                    else:167                        next_state = lstm(num_hidden_lstm_list[i],168                                          indata=hidden,169                                          prev_state=last_states[i],170                                          param=param_cells[i],171                                          seqidx=k,172                                          layeridx=i,173                                          dropout=dp,174                                          num_hidden_proj=num_hidden_proj,175                                          is_batchnorm=is_batchnorm,176                                          name=prefix + ("t%d_l%d" % (seqidx, i))177                                          )178                elif lstm_type == 'vanilla_lstm':179                    if is_batchnorm:180                        next_state = vanilla_lstm(num_hidden_lstm_list[i], indata=hidden,181                                                  prev_state=last_states[i],182                                                  param=param_cells[i],183                                                  seqidx=k, layeridx=i,184                                                  is_batchnorm=is_batchnorm,185                                                  gamma=batchnorm_gamma[i],186                                                  beta=batchnorm_beta[i],187                                                  name=prefix + ("t%d_l%d" % (seqidx, i))188                                                  )189                    else:190                        next_state = vanilla_lstm(num_hidden_lstm_list[i], indata=hidden,191                                                  prev_state=last_states[i],192                                                  param=param_cells[i],193                                                  seqidx=k, layeridx=i,194                                                  is_batchnorm=is_batchnorm,195                                                  name=prefix + ("t%d_l%d" % (seqidx, i))196                                                  )197                else:198                    raise Exception("lstm type %s error" % lstm_type)199                hidden = next_state.h200                last_states[i] = next_state201            # decoder202            if dropout > 0.:203                hidden = mx.sym.Dropout(data=hidden, p=dropout)204            if direction == "forward":205                hidden_all.append(hidden)206            elif direction == "backward":207                hidden_all.insert(0, hidden)208            else:209                raise Exception("direction should be whether forward or backward")210        net = hidden_all211    return net212def bi_lstm_unroll(net, num_lstm_layer, seq_len, num_hidden_lstm_list, dropout=0., num_hidden_proj=0,213                   lstm_type='fc_lstm', is_batchnorm=False, is_bucketing=False):214    if num_lstm_layer > 0:215        net_forward = lstm_unroll(net=net,216                                  num_lstm_layer=num_lstm_layer,217                                  seq_len=seq_len,218                                  num_hidden_lstm_list=num_hidden_lstm_list,219                                  dropout=dropout,220                                  num_hidden_proj=num_hidden_proj,221                                  lstm_type=lstm_type,222                                  is_batchnorm=is_batchnorm,223                                  prefix="forward_",224                                  direction="forward",225                                  is_bucketing=is_bucketing)226        net_backward = lstm_unroll(net=net,227                                   num_lstm_layer=num_lstm_layer,228                                   seq_len=seq_len,229                                   num_hidden_lstm_list=num_hidden_lstm_list,230                                   dropout=dropout,231                                   num_hidden_proj=num_hidden_proj,232                                   lstm_type=lstm_type,233                                   is_batchnorm=is_batchnorm,234                                   prefix="backward_",235                                   direction="backward",236                                   is_bucketing=is_bucketing)237        hidden_all = []238        for i in range(seq_len):239            hidden_all.append(mx.sym.Concat(*[net_forward[i], net_backward[i]], dim=1))240        net = hidden_all241    return net242# bilistm_2to1243def bi_lstm_unroll_two_input_two_output(net1, net2, num_lstm_layer, seq_len, num_hidden_lstm_list, dropout=0.,244                                        num_hidden_proj=0,245                                        lstm_type='fc_lstm',246                                        is_batchnorm=False,247                                        is_bucketing=False):248    if num_lstm_layer > 0:249        net_forward = lstm_unroll(net=net1,250                                  num_lstm_layer=num_lstm_layer,251                                  seq_len=seq_len,252                                  num_hidden_lstm_list=num_hidden_lstm_list,253                                  dropout=dropout,254                                  num_hidden_proj=num_hidden_proj,255                                  lstm_type=lstm_type,256                                  is_batchnorm=is_batchnorm,257                                  prefix="forward_",258                                  direction="forward",259                                  is_bucketing=is_bucketing)260        net_backward = lstm_unroll(net=net2,261                                   num_lstm_layer=num_lstm_layer,262                                   seq_len=seq_len,263                                   num_hidden_lstm_list=num_hidden_lstm_list,264                                   dropout=dropout,265                                   num_hidden_proj=num_hidden_proj,266                                   lstm_type=lstm_type,267                                   is_batchnorm=is_batchnorm,268                                   prefix="backward_",269                                   direction="backward",270                                   is_bucketing=is_bucketing)271        return net_forward, net_backward272    else:...

my_answers.py

Source:my_answers.py

1import numpy as np2class NeuralNetwork(object):3    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):4        # Set number of nodes in input, hidden and output layers.5        self.input_nodes = input_nodes6        self.hidden_nodes = hidden_nodes7        self.output_nodes = output_nodes8        # Initialize weights9        self.weights_input_to_hidden = np.random.normal(0.0,10                                                        self.input_nodes**-0.5,11                                                        (self.input_nodes, self.hidden_nodes))12        self.weights_hidden_to_output = np.random.normal(0.0,13                                                         self.hidden_nodes**-0.5,14                                                         (self.hidden_nodes, self.output_nodes))15        self.lr = learning_rate16        self.activation_function = self.sigmoid17    def sigmoid(self, x):18        return 1. / (1. + np.exp(-x))19    def train(self, features, targets):20        ''' Train the network on batch of features and targets.21            Arguments22            ---------23            features: 2D array, each row is one data record, each column is a feature24            targets: 1D array of target values25        '''26        n_records = features.shape[0]27        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)28        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)29        for X, y in zip(features, targets):30            # Implement the forward pass function below31            final_outputs, hidden_outputs = self.forward_pass_train(X)32            # Implement the backproagation function below33            delta_weights_i_h, delta_weights_h_o = self.backpropagation(final_outputs,34                                                                        hidden_outputs,35                                                                        X,36                                                                        y,37                                                                        delta_weights_i_h,38                                                                        delta_weights_h_o)39        self.update_weights(delta_weights_i_h, delta_weights_h_o, n_records)40    def forward_pass_train(self, X):41        ''' Implement forward pass here.42            Arguments43            ---------44            X: features batch45        '''46        hidden_inputs = np.dot(X, self.weights_input_to_hidden)47        hidden_outputs = self.activation_function(hidden_inputs)48        final_outputs = np.dot(hidden_outputs, self.weights_hidden_to_output)49        return final_outputs, hidden_outputs50    def backpropagation(self, final_outputs, hidden_outputs, X, y, delta_weights_i_h, delta_weights_h_o):51        ''' Implement backpropagation52            Arguments53            ---------54            final_outputs: output from forward pass55            y: target (i.e. label) batch56            delta_weights_i_h: change in weights from input to hidden layers57            delta_weights_h_o: change in weights from hidden to output layers58        '''59        # Output layer error is the difference between desired target and actual output.60        error = y - final_outputs61        # Calculate the hidden layer's contribution to the error62        hidden_error = np.dot(self.weights_hidden_to_output, error)63        # Backpropagated error terms64        output_error_term = error * 1.65        hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)66        # Weight step (input to hidden)67        delta_weights_i_h += hidden_error_term * X[:, None]68        # Weight step (hidden to output)69        delta_weights_h_o += output_error_term * hidden_outputs[:, None]70        return delta_weights_i_h, delta_weights_h_o71    def update_weights(self, delta_weights_i_h, delta_weights_h_o, n_records):72        ''' Update weights on gradient descent step73            Arguments74            ---------75            delta_weights_i_h: change in weights from input to hidden layers76            delta_weights_h_o: change in weights from hidden to output layers77            n_records: number of records78        '''79        # update hidden-to-output weights with gradient descent step80        self.weights_hidden_to_output += self.lr * delta_weights_h_o / n_records81        # update input-to-hidden weights with gradient descent step82        self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records83    def run(self, features):84        ''' Run a forward pass through the network with input features.85            Arguments86            ---------87            features: 1D array of feature values88        '''89        hidden_inputs = np.dot(features, self.weights_input_to_hidden)90        hidden_outputs = self.activation_function(hidden_inputs)91        final_outputs = np.dot(hidden_outputs, self.weights_hidden_to_output)92        return final_outputs93#########################################################94# Set your hyperparameters here95##########################################################96iterations = 300097learning_rate = 1.98hidden_nodes = 12...

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